Zhou Jingchun, Wei Xiaojing, Shi Jinyu, Chu Weishen, Lin Yi
Opt Express. 2022 May 9;30(10):17290-17306. doi: 10.1364/OE.450858.
Underwater images suffer color distortions and low contrast. This is because the light is absorbed and scattered when it travels through water. Different underwater scenes result in different color deviations and levels of detail loss in underwater images. To address these issues of color distortion and low contrast, an underwater image enhancement method that includes two-level wavelet decomposition maximum brightness color restoration, and edge refinement histogram stretching is proposed. First, according to the Jaffe-McGlamery underwater optical imaging model, the proportions of the maximum bright channel were obtained to correct the color of underwater images. Then, edge refinement histogram stretching was designed, and edge refinement and denoising processing were performed while stretching the histogram to enhance contrast and noise removal. Finally, wavelet two-level decomposition of the color-corrected and contrast-stretched underwater images was performed, and the decomposed components in equal proportions were fused. The proposed method can restore the color and detail and enhance the contrast of the underwater image. Extensive experiments demonstrated that the proposed method achieves superior performance against state-of-the-art methods in visual quality and quantitative metrics.
水下图像存在颜色失真和对比度低的问题。这是因为光线在水中传播时会被吸收和散射。不同的水下场景会导致水下图像出现不同程度的颜色偏差和细节损失。为了解决这些颜色失真和对比度低的问题,提出了一种水下图像增强方法,该方法包括两级小波分解、最大亮度颜色恢复和边缘细化直方图拉伸。首先,根据贾菲 - 麦格拉梅里水下光学成像模型,获取最大亮度通道的比例以校正水下图像的颜色。然后,设计边缘细化直方图拉伸,并在拉伸直方图的同时进行边缘细化和去噪处理,以增强对比度和去除噪声。最后,对颜色校正和对比度拉伸后的水下图像进行小波两级分解,并将分解后的等比例分量进行融合。所提出的方法可以恢复水下图像的颜色和细节,并增强其对比度。大量实验表明,所提出的方法在视觉质量和定量指标方面优于现有方法。